code: def eval_test(
      parser,
      program,
      model,
      test_exe,
      graph,
      y_true,
      split_idx,
      partition,
  ):
      y_pred = np.zeros_like(y_true)
  
      for subgraph, feed_dict in partition:
          batch_y_pred = test_exe.run(
              program=program,
              feed=feed_dict,
              fetch_list=[model.out_feat],
          )[0]
  
          y_pred[subgraph.node_feat["nid"]] = batch_y_pred
  
      train_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["train"]],
              "y_pred": y_pred[split_idx["train"]],
          }
      )["rocauc"]
      val_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["valid"]],
              "y_pred": y_pred[split_idx["valid"]],
          }
      )["rocauc"]
      test_acc = evaluator.eval(
          {
              "y_true": y_true[split_idx["test"]],
              "y_pred": y_pred[split_idx["test"]],
          }
      )["rocauc"]
  
      return train_acc, val_acc, test_acc
  
imports: import math
  import math
  import ssl
  from sklearn.metrics import roc_auc_score
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  import time
  import copy
  from ogb.nodeproppred import Evaluator
  import pickle
  from model import Proteins_baseline_model, Proteins_label_embedding_model
  from partition import RandomPartition
  from cross_edge_feat import cross_edge_feat
  from dataset.random_partition_dataset import RandomPartition
  import os
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling a global function with example usage: eval_test(parser, program, model, test_exe, graph, y_true, split_idx, partition) and returns: train_acc,  val_acc,  test_acc